Kling omni image supports both text-to-image and image-to-image creation, and works especially well when subject, product, or material consistency matters across iterations.
Keep prompts compact and concrete, then add references only when they help maintain subject, product, or styling consistency.
High-saturation commercial snack ad with an orange-yellow gradient studio background, floating green leaves, and rippled chips. Three floating cylindrical chip cans in a triangular composition, center facing front and sides tilted. Bright orange packaging, white lids, metallic bases, and chip graphics. Realistic photography, bright soft highlights, clean frame, strong impact. For product posters and ecommerce hero visuals.
Keep the person's facial features, identity, over-the-shoulder pose, half-body framing, gray studio background, and soft light unchanged. Change the black long hair to long silver-white waves. Replace the white off-shoulder dress with a black futuristic tactical battle suit with leather texture, metallic shoulder armor, and tech details. Present the result as a realistic commercial-photo sci-fi character upgrade.
Start from the publishing use case, then decide whether the next iteration needs more references or stronger polish.
Best when the visual needs room for the product, atmosphere, and future messaging.
Useful for validating subject focus, campaign mood, and visual consistency quickly.
Use a more polished pass when material detail, clean edges, and retouch headroom matter most.
This tool uses third-party model capabilities and is not affiliated with or endorsed by the original model providers unless explicitly stated.
It works well for still-image workflows that need both text-to-image and image-to-image generation with strong reference support and subject consistency.
The current interface supports up to 4 reference images, and each uploaded image must be 20MB or smaller.
It is a good fit for product stills, character refinement, social ad variants, and other image tasks where visual consistency matters across iterations.
Choose Kling omni image when reference control, subject consistency, and image-to-image stability matter more. Choose Seedream when text-to-image generation, high-resolution output, or dense information design is the priority.